Example usage for java.io ObjectInputStream close

List of usage examples for java.io ObjectInputStream close

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Introduction

In this page you can find the example usage for java.io ObjectInputStream close.

Prototype

public void close() throws IOException

Source Link

Document

Closes the input stream.

Usage

From source file:cacheservice.CacheServer.java

/**
 * @param args the command line arguments
 *//*from   w  w w .  j a  v a 2  s .co  m*/
public static void main(String[] args) {
    // COMUNICACIN CON EL CLIENTE
    ServerSocket serverSocket;
    Socket socketCliente;
    DataInputStream in; //Flujo de datos de entrada
    DataOutputStream out; //Flujo de datos de salida
    String mensaje;
    int laTengoenCache = 0;

    //COMUNICACIN CON EL INDEX
    ServerSocket serverSocketIndex;
    Socket socketIndex;
    DataOutputStream outIndex;
    ObjectInputStream inIndex;
    String mensajeIndex;

    try {
        serverSocket = new ServerSocket(4444);
        System.out.print("SERVIDOR CACHE ACTIVO a la espera de peticiones");

        //MIENTRAS PERMANEZCA ACTIVO EL SERVIDOR CACHE ESPERAR? POR PETICIONES DE LOS CLIENTES
        while (true) {
            socketCliente = serverSocket.accept();
            in = new DataInputStream(socketCliente.getInputStream()); //Entrada de los mensajes del cliente
            mensaje = in.readUTF(); //Leo el mensaje enviado por el cliente

            System.out.println("\nHe recibido del cliente: " + mensaje); //Muestro el mensaje recibido por el cliente
            //int particionBuscada = seleccionarParticion(mensaje, tamanoCache, numeroParticiones); //Busco la particin
            //double tamanoParticion = Math.ceil( (double)tamanoCache / (double)numeroParticiones);

            //Thread hilo = new Hilo(mensaje,particionBuscada,cache.GetTable(),(int) tamanoParticion);
            //hilo.start();

            //RESPUESTA DEL SERVIDOR CACHE AL CLIENTE
            out = new DataOutputStream(socketCliente.getOutputStream());
            String respuesta = "Respuesta para " + mensaje;
            if (laTengoenCache == 1) {
                out.writeUTF(respuesta);
                System.out.println("\nTengo la respuesta. He respondido al cliente: " + respuesta);
            } else {
                out.writeUTF("miss");
                out.close();
                in.close();
                socketCliente.close();

                System.out.println("\nNo tengo la respuesta.");

                //LEER RESPUESTA DEL SERVIDOR INDEX
                serverSocketIndex = new ServerSocket(6666);
                socketIndex = serverSocketIndex.accept();
                inIndex = new ObjectInputStream(socketIndex.getInputStream());
                JSONObject mensajeRecibidoIndex = (JSONObject) inIndex.readObject();

                System.out.println("He recibido del SERVIDOR INDEX: " + mensajeRecibidoIndex);

                //outIndex.close();
                inIndex.close();
                socketIndex.close();

            }

        }
    } catch (Exception e) {
        System.out.print(e.getMessage());
    }
}

From source file:com.yahoo.labs.yamall.local.Yamall.java

public static void main(String[] args) {
    String[] remainingArgs = null;
    String inputFile = null;/* w  ww .j av a 2s.c o m*/
    String predsFile = null;
    String saveModelFile = null;
    String initialModelFile = null;
    String lossName = null;
    String parserName = null;
    String linkName = null;
    String invertHashName = null;
    double learningRate = 1;
    String minPredictionString = null;
    String maxPredictionString = null;
    String fmNumberFactorsString = null;
    int bitsHash;
    int numberPasses;
    int holdoutPeriod = 10;

    boolean testOnly = false;
    boolean exponentialProgress;
    double progressInterval;

    options.addOption("h", "help", false, "displays this help");
    options.addOption("t", false, "ignore label information and just test");
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("binary")
            .desc("reports loss as binary classification with -1,1 labels").build());
    options.addOption(
            Option.builder().hasArg(false).required(false).longOpt("solo").desc("uses SOLO optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("pcsolo")
            .desc("uses Per Coordinate SOLO optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("pistol")
            .desc("uses PiSTOL optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("kt")
            .desc("(EXPERIMENTAL) uses KT optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("pckt")
            .desc("(EXPERIMENTAL) uses Per Coordinate KT optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("pccocob")
            .desc("(EXPERIMENTAL) uses Per Coordinate COCOB optimizer").build());
    options.addOption(Option.builder().hasArg(false).required(false).longOpt("cocob")
            .desc("(EXPERIMENTAL) uses COCOB optimizer").build());
    options.addOption(
            Option.builder().hasArg(false).required(false).longOpt("fm").desc("Factorization Machine").build());
    options.addOption(Option.builder("f").hasArg(true).required(false).desc("final regressor to save")
            .type(String.class).longOpt("final_regressor").build());
    options.addOption(Option.builder("p").hasArg(true).required(false).desc("file to output predictions to")
            .longOpt("predictions").type(String.class).build());
    options.addOption(
            Option.builder("i").hasArg(true).required(false).desc("initial regressor(s) to load into memory")
                    .longOpt("initial_regressor").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false).desc(
            "specify the loss function to be used. Currently available ones are: absolute, squared (default), hinge, logistic")
            .longOpt("loss_function").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false).desc(
            "specify the link function used in the output of the predictions. Currently available ones are: identity (default), logistic")
            .longOpt("link").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("output human-readable final regressor with feature names").longOpt("invert_hash")
            .type(String.class).build());
    options.addOption(
            Option.builder("l").hasArg(true).required(false).desc("set (initial) learning Rate, default = 1.0")
                    .longOpt("learning_rate").type(String.class).build());
    options.addOption(Option.builder("b").hasArg(true).required(false)
            .desc("number of bits in the feature table, default = 18").longOpt("bit_precision")
            .type(String.class).build());
    options.addOption(Option.builder("P").hasArg(true).required(false)
            .desc("progress update frequency, integer: additive; float: multiplicative, default = 2.0")
            .longOpt("progress").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("smallest prediction to output, before the link function, default = -50")
            .longOpt("min_prediction").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("smallest prediction to output, before the link function, default = 50")
            .longOpt("max_prediction").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("ignore namespaces beginning with the characters in <arg>").longOpt("ignore")
            .type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false).desc("number of training passes")
            .longOpt("passes").type(String.class).build());
    options.addOption(
            Option.builder().hasArg(true).required(false).desc("holdout period for test only, default = 10")
                    .longOpt("holdout_period").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("number of factors for Factorization Machines default = 8").longOpt("fmNumberFactors")
            .type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false)
            .desc("specify the parser to use. Currently available ones are: vw (default), libsvm, tsv")
            .longOpt("parser").type(String.class).build());
    options.addOption(Option.builder().hasArg(true).required(false).desc("schema file for the TSV input")
            .longOpt("schema").type(String.class).build());

    CommandLineParser parser = new DefaultParser();
    CommandLine cmd = null;
    try {
        cmd = parser.parse(options, args);
    } catch (ParseException e) {
        System.out.println("Unrecognized option");
        help();
    }
    if (cmd.hasOption("h"))
        help();
    if (cmd.hasOption("t"))
        testOnly = true;
    if (cmd.hasOption("binary")) {
        binary = true;
        System.out.println("Reporting binary loss");
    }
    initialModelFile = cmd.getOptionValue("i");
    predsFile = cmd.getOptionValue("p");
    lossName = cmd.getOptionValue("loss_function", "squared");
    linkName = cmd.getOptionValue("link", "identity");
    saveModelFile = cmd.getOptionValue("f");
    learningRate = Double.parseDouble(cmd.getOptionValue("l", "1.0"));
    bitsHash = Integer.parseInt(cmd.getOptionValue("b", "18"));
    invertHashName = cmd.getOptionValue("invert_hash");
    minPredictionString = cmd.getOptionValue("min_prediction", "-50");
    maxPredictionString = cmd.getOptionValue("max_prediction", "50");
    fmNumberFactorsString = cmd.getOptionValue("fmNumberFactors", "8");
    parserName = cmd.getOptionValue("parser", "vw");

    numberPasses = Integer.parseInt(cmd.getOptionValue("passes", "1"));
    System.out.println("Number of passes = " + numberPasses);
    if (numberPasses > 1) {
        holdoutPeriod = Integer.parseInt(cmd.getOptionValue("holdout_period", "10"));
        System.out.println("Holdout period = " + holdoutPeriod);
    }

    remainingArgs = cmd.getArgs();
    if (remainingArgs.length == 1)
        inputFile = remainingArgs[0];

    InstanceParser instanceParser = null;
    if (parserName.equals("vw"))
        instanceParser = new VWParser(bitsHash, cmd.getOptionValue("ignore"), (invertHashName != null));
    else if (parserName.equals("libsvm"))
        instanceParser = new LIBSVMParser(bitsHash, (invertHashName != null));
    else if (parserName.equals("tsv")) {
        String schema = cmd.getOptionValue("schema");
        if (schema == null) {
            System.out.println("TSV parser requires a schema file.");
            System.exit(0);
        } else {
            String spec = null;
            try {
                spec = new String(Files.readAllBytes(Paths.get(schema)));
            } catch (IOException e) {
                System.out.println("Error reading the TSV schema file.");
                e.printStackTrace();
                System.exit(0);
            }
            instanceParser = new TSVParser(bitsHash, cmd.getOptionValue("ignore"), (invertHashName != null),
                    spec);
        }
    } else {
        System.out.println("Unknown parser.");
        System.exit(0);
    }
    System.out.println("Num weight bits = " + bitsHash);

    // setup progress
    String progress = cmd.getOptionValue("P", "2.0");
    if (progress.indexOf('.') >= 0) {
        exponentialProgress = true;
        progressInterval = (double) Double.parseDouble(progress);
    } else {
        exponentialProgress = false;
        progressInterval = (double) Integer.parseInt(progress);
    }

    // min and max predictions
    minPrediction = (double) Double.parseDouble(minPredictionString);
    maxPrediction = (double) Double.parseDouble(maxPredictionString);

    // number of factors for Factorization Machines
    fmNumberFactors = (int) Integer.parseInt(fmNumberFactorsString);

    // configure the learner
    Loss lossFnc = null;
    LinkFunction link = null;
    if (initialModelFile == null) {
        if (cmd.hasOption("kt")) {
            learner = new KT(bitsHash);
        } else if (cmd.hasOption("pckt")) {
            learner = new PerCoordinateKT(bitsHash);
        } else if (cmd.hasOption("pcsolo")) {
            learner = new PerCoordinateSOLO(bitsHash);
        } else if (cmd.hasOption("solo")) {
            learner = new SOLO(bitsHash);
        } else if (cmd.hasOption("pccocob")) {
            learner = new PerCoordinateCOCOB(bitsHash);
        } else if (cmd.hasOption("cocob")) {
            learner = new COCOB(bitsHash);
        } else if (cmd.hasOption("pistol")) {
            learner = new PerCoordinatePiSTOL(bitsHash);
        } else if (cmd.hasOption("fm")) {
            learner = new SGD_FM(bitsHash, fmNumberFactors);
        } else
            learner = new SGD_VW(bitsHash);
    } else {
        learner = IOLearner.loadLearner(initialModelFile);
    }

    // setup link function
    if (linkName.equals("identity")) {
        link = new IdentityLinkFunction();
    } else if (linkName.equals("logistic")) {
        link = new LogisticLinkFunction();
    } else {
        System.out.println("Unknown link function.");
        System.exit(0);
    }

    // setup loss function
    if (lossName.equals("squared")) {
        lossFnc = new SquareLoss();
    } else if (lossName.equals("hinge")) {
        lossFnc = new HingeLoss();
    } else if (lossName.equals("logistic")) {
        lossFnc = new LogisticLoss();
    } else if (lossName.equals("absolute")) {
        lossFnc = new AbsLoss();
    } else {
        System.out.println("Unknown loss function.");
        System.exit(0);
    }

    learner.setLoss(lossFnc);
    learner.setLearningRate(learningRate);

    // maximum range predictions
    System.out.println("Max prediction = " + maxPrediction + ", Min Prediction = " + minPrediction);
    // print information about the learner
    System.out.println(learner.toString());
    // print information about the link function
    System.out.println(link.toString());
    // print information about the parser
    System.out.println(instanceParser.toString());
    // print information about ignored namespaces
    System.out.println("Ignored namespaces = " + cmd.getOptionValue("ignore", ""));

    long start = System.nanoTime();
    FileInputStream fstream;
    try {
        BufferedReader br = null;
        if (inputFile != null) {
            fstream = new FileInputStream(inputFile);
            System.out.println("Reading datafile = " + inputFile);
            br = new BufferedReader(new InputStreamReader(fstream));
        } else {
            System.out.println("Reading from console");
            br = new BufferedReader(new InputStreamReader(System.in));
        }

        File fout = null;
        FileOutputStream fos = null;
        BufferedWriter bw = null;
        if (predsFile != null) {
            fout = new File(predsFile);
            fos = new FileOutputStream(fout);
            bw = new BufferedWriter(new OutputStreamWriter(fos));
        }

        try {
            System.out.println("average       example  current  current  current");
            System.out.println("loss          counter    label  predict  features");
            int iter = 0;
            double cumLoss = 0;
            double weightedSampleSum = 0;
            double sPlus = 0;
            double sMinus = 0;
            Instance sample = null;
            boolean justPrinted = false;
            int pass = 0;
            ObjectOutputStream ooutTr = null;
            ObjectOutputStream ooutHO = null;
            ObjectInputStream oinTr = null;
            double pred = 0;
            int limit = 1;
            double hError = Double.MAX_VALUE;
            double lastHError = Double.MAX_VALUE;
            int numTestSample = 0;
            int numTrainingSample = 0;
            int idx = 0;

            if (numberPasses > 1) {
                ooutTr = new ObjectOutputStream(new FileOutputStream("cache_training.bin"));
                ooutHO = new ObjectOutputStream(new FileOutputStream("cache_holdout.bin"));
                oinTr = new ObjectInputStream(new FileInputStream("cache_training.bin"));
            }

            do {
                while (true) {
                    double score;

                    if (pass > 0 && numberPasses > 1) {
                        Instance tmp = (Instance) oinTr.readObject();
                        if (tmp != null)
                            sample = tmp;
                        else
                            break;
                    } else {
                        String strLine = br.readLine();
                        if (strLine != null)
                            sample = instanceParser.parse(strLine);
                        else
                            break;
                    }

                    justPrinted = false;
                    idx++;

                    if (numberPasses > 1 && pass == 0 && idx % holdoutPeriod == 0) {
                        // store the current sample for the holdout set
                        ooutHO.writeObject(sample);
                        ooutHO.reset();
                        numTestSample++;
                    } else {
                        if (numberPasses > 1 && pass == 0) {
                            ooutTr.writeObject(sample);
                            ooutTr.reset();
                            numTrainingSample++;
                        }

                        iter++;
                        if (testOnly) {
                            // predict the sample
                            score = learner.predict(sample);
                        } else {
                            // predict the sample and update the classifier using the sample
                            score = learner.update(sample);
                        }
                        score = Math.min(Math.max(score, minPrediction), maxPrediction);
                        pred = link.apply(score);
                        if (!binary)
                            cumLoss += learner.getLoss().lossValue(score, sample.getLabel())
                                    * sample.getWeight();
                        else if (Math.signum(score) != sample.getLabel())
                            cumLoss += sample.getWeight();

                        weightedSampleSum += sample.getWeight();
                        if (sample.getLabel() > 0)
                            sPlus = sPlus + sample.getWeight();
                        else
                            sMinus = sMinus + sample.getWeight();

                        // output predictions to file
                        if (predsFile != null) {
                            bw.write(String.format("%.6f %s", pred, sample.getTag()));
                            bw.newLine();
                        }

                        // print statistics to screen
                        if (iter == limit) {
                            justPrinted = true;
                            System.out.printf("%.6f %12d  % .4f  % .4f  %d\n", cumLoss / weightedSampleSum,
                                    iter, sample.getLabel(), pred, sample.getVector().size());
                            if (exponentialProgress)
                                limit *= progressInterval;
                            else
                                limit += progressInterval;
                        }
                    }
                }
                if (numberPasses > 1) {
                    if (pass == 0) { // finished first pass of many
                        // write a null at the end of the files
                        ooutTr.writeObject(null);
                        ooutHO.writeObject(null);
                        ooutTr.flush();
                        ooutHO.flush();
                        ooutTr.close();
                        ooutHO.close();

                        System.out.println("finished first epoch");
                        System.out.println(numTrainingSample + " training samples");
                        System.out.println(numTestSample + " holdout samples saved");
                    }
                    lastHError = hError;
                    hError = evalHoldoutError();
                }
                if (numberPasses > 1) {
                    System.out.printf("Weighted loss on holdout on epoch %d = %.6f\n", pass + 1, hError);

                    oinTr.close();
                    oinTr = new ObjectInputStream(new FileInputStream("cache_training.bin"));

                    if (hError > lastHError) {
                        System.out.println("Early stopping");
                        break;
                    }
                }
                pass++;
            } while (pass < numberPasses);

            if (justPrinted == false) {
                System.out.printf("%.6f %12d  % .4f  % .4f  %d\n", cumLoss / weightedSampleSum, iter,
                        sample.getLabel(), pred, sample.getVector().size());
            }
            System.out.println("finished run");

            System.out.println(String.format("average loss best constant predictor: %.6f",
                    lossFnc.lossConstantBinaryLabels(sPlus, sMinus)));

            if (saveModelFile != null)
                IOLearner.saveLearner(learner, saveModelFile);
            if (invertHashName != null)
                IOLearner.saveInvertHash(learner.getWeights(), instanceParser.getInvertHashMap(),
                        invertHashName);
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        } catch (ClassNotFoundException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }

        // close the input stream
        try {
            br.close();
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        // close the output stream
        if (predsFile != null) {
            try {
                bw.close();
            } catch (IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
        }
        long millis = System.nanoTime() - start;
        System.out.printf("Elapsed time: %d min, %d sec\n", TimeUnit.NANOSECONDS.toMinutes(millis),
                TimeUnit.NANOSECONDS.toSeconds(millis) - 60 * TimeUnit.NANOSECONDS.toMinutes(millis));
    } catch (

    FileNotFoundException e) {
        System.out.println("Error opening the input file");
        e.printStackTrace();
    }

}

From source file:Main.java

static public Object setObjectBytes(byte[] b) throws IOException, ClassNotFoundException {
    ByteArrayInputStream baos = new ByteArrayInputStream(b);
    ObjectInputStream oos = new ObjectInputStream(baos);
    oos.close();
    return oos.readObject();
}

From source file:Main.java

public static Object deserializar(String path) throws Exception {
    FileInputStream inFile = new FileInputStream(path);
    ObjectInputStream d = new ObjectInputStream(inFile);
    Object o = d.readObject();/*from   w  w w.  j a  v a 2s .  co m*/
    d.close();
    return o;
}

From source file:Main.java

private static Object readFromFile(String filename) throws Exception {
    ObjectInputStream ois = new ObjectInputStream(new FileInputStream(new File(filename)));
    Object object = ois.readObject();
    ois.close();
    return object;
}

From source file:Main.java

public static Object restore(String fileName) throws IOException, ClassNotFoundException {
    FileInputStream fis = new FileInputStream(fileName);
    ObjectInputStream in = new ObjectInputStream(fis);
    Object obj = in.readObject();
    in.close();
    return obj;// w w  w .j a  va  2 s. c o  m
}

From source file:Main.java

@SuppressWarnings("unchecked")
public static <T> T readSerializedObject(Context context, String fileName)
        throws FileNotFoundException, IOException, ClassNotFoundException {
    FileInputStream fis = context.openFileInput(fileName);
    ObjectInputStream in = new ObjectInputStream(fis);

    T out = (T) in.readObject();/*from  w w w. j  a  v a2 s . co  m*/

    in.close();
    fis.close();
    return out;
}

From source file:Main.java

public static Object readFileByObject(String fileName) {
    try {//ww w  . j a v a2 s.  com
        FileInputStream fis = new FileInputStream(fileName);
        ObjectInputStream ois = new ObjectInputStream(fis);
        Object o = ois.readObject();
        ois.close();
        fis.close();
        return o;
    } catch (Exception e) {
        return null;
    }
}

From source file:Main.java

public static Object getSerializable(byte[] bytes) {
    Object obj = null;//ww w.  ja v  a 2 s  .c o  m
    try {
        ByteArrayInputStream bis = new ByteArrayInputStream(bytes);
        ObjectInputStream ois = new ObjectInputStream(bis);
        obj = ois.readObject();
        ois.close();
        bis.close();
    } catch (IOException ex) {
        ex.printStackTrace();
    } catch (ClassNotFoundException ex) {
        ex.printStackTrace();
    }
    return obj;
}

From source file:MainClass.java

public static void desEncrypt(String f1, String f2) throws Exception {
    SecretKey key = null;/*w  w  w. j  ava  2  s .c  o  m*/

    ObjectInputStream keyFile = new ObjectInputStream(new FileInputStream("DESKey.ser"));
    key = (SecretKey) keyFile.readObject();
    keyFile.close();
    Cipher cipher = Cipher.getInstance("DES/ECB/PKCS5Padding");
    cipher.init(Cipher.DECRYPT_MODE, key);
    CipherInputStream in = new CipherInputStream(new BufferedInputStream(new FileInputStream(f1)), cipher);
    BufferedOutputStream out = new BufferedOutputStream(new FileOutputStream(f2));
    int i;
    do {
        i = in.read();
        if (i != -1)
            out.write(i);
    } while (i > 0);
    in.close();
    out.close();
}